This data notebook is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
If you want to cite the method/model please use:
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.
If you are interested in how the model works start from https://dataknut.github.io/localCarbonTaxModels/
If you wish to re-use material from this data notebook please cite it as:
Ben Anderson (2021) Data notebook: Simulating a local emissions levy to fund local energy effiency retrofit: Newham. University of Southampton, United Kingdom
License: CC-BY
Share, adapt, give attribution.
This data notebook estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.
It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.
Key results:
This data notebook estimates a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator.
The model applies carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. It then sums these values to given an overall levy revenue estimate for the area in the case study.
The data notebook then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
Finally the data notebook compares the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so the data notebook also analyses the extent to which redistribution of revenue from high emissions areas (households) would be required.
It should be noted that this area level analysis uses mean emissions per household. It will therefore not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy values that might be expected.
NB: no maps in the interests of speed
The model uses a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions.
All analysis is at LSOA level. Cautions on inference from area level data apply.
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
region | nLSOAs | mean_KgCo2ePerCap | sd_KgCo2ePerCap |
London | 162 | 6,386.9 | 1,488.6 |
Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 037A Royal Docks 1034 1861 1700
## 2: Newham 022D Plaistow South 835 941 661
## 3: Newham 030C Canning Town North 830 818 439
## 4: Newham 012C Stratford and New Town 808 860 590
## 5: Newham 009D Forest Gate South 801 939 637
## 6: Newham 031C Canning Town South 791 806 587
## LSOA11NM WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 013G Stratford and New Town 731 6351 6350
## 2: Newham 037E Royal Docks 574 3116 2900
## 3: Newham 037A Royal Docks 1034 1861 1700
## 4: Newham 033B Beckton 406 1686 1360
## 5: Newham 013E Stratford and New Town 154 1671 1470
## 6: Newham 034H Canning Town South 191 1585 1470
LSOA11NM | WD18NM | nGasMeters | nElecMeters | epc_total |
Newham 037A | Royal Docks | 1,034 | 1,861 | 1,700 |
Newham 022D | Plaistow South | 835 | 941 | 661 |
Newham 030C | Canning Town North | 830 | 818 | 439 |
Newham 012C | Stratford and New Town | 808 | 860 | 590 |
Newham 009D | Forest Gate South | 801 | 939 | 637 |
Newham 031C | Canning Town South | 791 | 806 | 587 |
Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.
Check that the assumption seems sensible…
Check for outliers - what might this indicate?
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values
| Name | …[] |
| Number of rows | 162 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 20459.41 | 5650.39 | 3587.62 | 16921.59 | 20949.11 | 24307.81 | 32968.22 | ▁▂▇▇▂ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2231.99 | 678.71 | 139.82 | 1862.83 | 2323.19 | 2744.06 | 3622.65 | ▁▂▇▇▃ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 929.81 | 130.64 | 553.10 | 835.82 | 916.78 | 1002.95 | 1347.45 | ▁▆▇▂▁ |
| CREDSmeasuredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3161.80 | 736.65 | 895.16 | 2730.02 | 3171.34 | 3730.70 | 4945.02 | ▁▂▇▇▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 160.29 | 65.09 | 17.41 | 118.72 | 154.48 | 193.10 | 451.65 | ▃▇▃▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3322.09 | 736.66 | 912.57 | 2892.58 | 3316.06 | 3880.60 | 5088.34 | ▁▁▇▇▂ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 1283.90 | 400.86 | 161.79 | 1048.16 | 1291.32 | 1540.35 | 2298.04 | ▁▃▇▅▁ |
| CREDSvan_kgco2e2018_pdw | 0 | 1 | 152.66 | 166.81 | 22.91 | 83.23 | 119.16 | 168.13 | 1665.36 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 0 | 1 | 1436.55 | 446.06 | 203.93 | 1165.89 | 1445.35 | 1739.86 | 2959.22 | ▁▆▇▂▁ |
Examine patterns of per dwelling emissions for sense.
Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.1: Scatter of LSOA level all consumption emissions per dwelling against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -5.5899, df = 160, p-value = 9.588e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5256456 -0.2666358
## sample estimates:
## cor
## -0.4042125
## Total emissions per dwelling (LSOA level) summary
## LSOA11CD WD18NM IMD_Decile_label All_Tco2e_per_dw
## Length:162 Length:162 3 :75 Min. : 3.588
## Class :character Class :character 2 :40 1st Qu.:16.922
## Mode :character Mode :character 4 :32 Median :20.949
## 5 : 9 Mean :20.459
## 1 (10% most deprived): 4 3rd Qu.:24.308
## 6 : 1 Max. :32.968
## (Other) : 1
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01003575 | Little Ilford | 3 | 33.0 |
E01033580 | Royal Docks | 8 | 32.6 |
E01003555 | Forest Gate South | 4 | 32.0 |
E01003561 | Green Street East | 4 | 31.1 |
E01003621 | Wall End | 4 | 30.9 |
E01003585 | Manor Park | 3 | 30.6 |
LSOA11CD | WD18NM | IMD_Decile_label | All_Tco2e_per_dw |
E01033583 | Stratford and New Town | 3 | 3.6 |
E01033577 | Royal Docks | 5 | 7.3 |
E01003577 | Little Ilford | 2 | 7.5 |
E01003488 | Boleyn | 2 | 8.4 |
E01033585 | Canning Town South | 2 | 9.1 |
E01003633 | West Ham | 2 | 9.1 |
Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.
## Per dwelling T CO2e - gas emissions
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 139.8 1862.8 2323.2 2232.0 2744.1 3622.7
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -2.3581, df = 160, p-value = 0.01958
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32818766 -0.02991636
## sample estimates:
## cor
## -0.1832664
Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.4732, df = 160, p-value = 0.01444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33614037 -0.03884478
## sample estimates:
## cor
## -0.1918909
Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.4732, df = 160, p-value = 0.01444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33614037 -0.03884478
## sample estimates:
## cor
## -0.1918909
RUC11 | mean_gas_kgco2e | mean_elec_kgco2e | mean_other_energy_kgco2e |
Urban major conurbation | 2,232.0 | 929.8 | 160.3 |
Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 12.698, df = 160, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6222617 0.7777135
## sample estimates:
## cor
## 0.7084787
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 12.558, df = 160, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6174264 0.7745915
## sample estimates:
## cor
## 0.704546
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
How does the correlation look now?
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -3.2064, df = 160, p-value = 0.001623
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.38531035 -0.09512201
## sample estimates:
## cor
## -0.2457135
RUC11 | mean_car_kgco2e | mean_van_kgco2e |
Urban major conurbation | 1,283.9 | 152.7 |
Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.76693, df = 160, p-value = 0.4443
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09455784 0.21273030
## sample estimates:
## cor
## 0.06052002
Case studies:
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
Table 4.6 below shows the overall £ GBP total for the case study area in £M under Scenario 1.
nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
162 | 518.8 | 55.5 | 25.7 |
region | nLSOAs | beis_GBPtotal_c | beis_total_c_gas | beis_GBPtotal_c_elec |
London | 162 | 518.8 | 55.5 | 25.7 |
Figure 4.7: Proportion of total emissions due to gas & electricity use by region covered
The table below shows the mean per dwelling value rounded to the nearest £10.
All_emissions | Gas | Electricity | Gas + Electricity |
5,012.6 | 546.8 | 227.8 | 774.6 |
Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.8: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.9: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 879 4146 5133 5013 5955 8077
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01003575 | Newham 005B | Little Ilford | 33.0 | 8,077.2 |
E01033580 | Newham 037F | Royal Docks | 32.6 | 7,993.7 |
E01003555 | Newham 007B | Forest Gate South | 32.0 | 7,847.3 |
E01003561 | Newham 007C | Green Street East | 31.1 | 7,631.8 |
E01003621 | Newham 023C | Wall End | 30.9 | 7,581.2 |
E01003585 | Newham 004B | Manor Park | 30.6 | 7,507.6 |
LSOA11CD | LSOA01NM | WD18NM | T_CO2e_pdw | GBP_allEmissions_levy |
E01033583 | Newham 013G | Stratford and New Town | 3.6 | 879.0 |
E01033577 | Newham 037E | Royal Docks | 7.3 | 1,796.7 |
E01003577 | Newham 005C | Little Ilford | 7.5 | 1,826.7 |
E01003488 | Newham 019A | Boleyn | 8.4 | 2,047.2 |
E01033585 | Newham 034J | Canning Town South | 9.1 | 2,233.1 |
E01003633 | Newham 020C | West Ham | 9.1 | 2,234.8 |
Figure ?? repeats the analysis but just for gas.
Anything unusual?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.11: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 34.25 456.39 569.18 546.84 672.29 887.55
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01003555 | Newham 007B | Forest Gate South | 3.6 | 887.5 |
E01003532 | Newham 010D | East Ham North | 3.3 | 809.1 |
E01003529 | Newham 010A | East Ham North | 3.3 | 804.4 |
E01003572 | Newham 008E | Green Street West | 3.3 | 800.5 |
E01003530 | Newham 010B | East Ham North | 3.2 | 796.1 |
E01003531 | Newham 010C | East Ham North | 3.2 | 793.9 |
LSOA11CD | LSOA01NM | WD18NM | gas_T_CO2e_pdw | GBP_gas_levy_perdw |
E01033577 | Newham 037E | Royal Docks | 0.3 | 83.8 |
E01033582 | Newham 037H | Royal Docks | 0.3 | 70.9 |
E01033576 | Newham 034H | Canning Town South | 0.3 | 68.4 |
E01033583 | Newham 013G | Stratford and New Town | 0.3 | 66.9 |
E01033579 | Newham 013F | Stratford and New Town | 0.3 | 63.4 |
E01033578 | Newham 013E | Stratford and New Town | 0.1 | 34.3 |
Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.13: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 135.5 204.8 224.6 227.8 245.7 330.1
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01003484 | Newham 032B | Beckton | 1.3 | 330.1 |
E01033582 | Newham 037H | Royal Docks | 1.3 | 326.0 |
E01003482 | Newham 033B | Beckton | 1.3 | 324.2 |
E01003555 | Newham 007B | Forest Gate South | 1.3 | 324.0 |
E01003572 | Newham 008E | Green Street West | 1.2 | 306.1 |
E01003531 | Newham 010C | East Ham North | 1.2 | 297.4 |
LSOA11CD | LSOA01NM | WD18NM | elec_T_CO2e_pdw | GBP_elec_levy_perdw |
E01033579 | Newham 013F | Stratford and New Town | 0.7 | 182.0 |
E01033580 | Newham 037F | Royal Docks | 0.7 | 180.8 |
E01033583 | Newham 013G | Stratford and New Town | 0.7 | 176.9 |
E01003577 | Newham 005C | Little Ilford | 0.7 | 176.5 |
E01033576 | Newham 034H | Canning Town South | 0.6 | 152.0 |
E01033577 | Newham 037E | Royal Docks | 0.6 | 135.5 |
Figure ?? shows the same analysis for measured energy (elec + gas)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.15: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 219.3 668.9 777.0 774.6 914.0 1211.5
Applied to per dwelling values (not LSOA total) - may be methodologically dubious?
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw
## 0% 25% 50% 75% 100%
## 3587.624 16921.591 20949.113 24307.812 32968.220
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Table: (#tab:estimateAnnualLevyScenario2Total)Data summary
| Name | …[] |
| Number of rows | 162 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 20.46 | 5.65 | 3.59 | 16.92 | 20.95 | 24.31 | 32.97 | ▁▂▇▇▂ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 3291.62 | 1512.63 | 437.69 | 2064.67 | 3052.37 | 4283.82 | 7462.19 | ▃▇▅▃▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2024777.26 | 640386.27 | 692179.20 | 1600139.17 | 1994368.87 | 2413701.30 | 4842460.68 | ▃▇▃▁▁ |
nLSOAs | sum_total_sc1 | sum_total_sc2 |
162 | 518.8 | 328.0 |
Figure 4.16 compares the % £ levy under each scenario for all consumption contributed by LSOAs in each IMD decile.
Figure 4.16: Comparing £ levy under each scenario by IMD decile - all consumption emissions
Figure 4.17 compares the £ levy under each scenario for all consumption.
Figure 4.17: Comparing £ levy under each scenario - all consumption emissions
## [1] 35.59282
## [1] 16.23055
nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP |
162 | 328.0 | 35.6 | 16.2 |
region | nLSOAs | sumAllConsEmissions_GBP | sumGasEmissions_GBP | sumElecEmissions_GBP | sumPop |
London | 162 | 328.0 | 35.6 | 16.2 | 348,960 |
Figure 4.18: Contribution to sum levy £ GBP by source
Source: English Housing Survey 2018 Energy Report
Model excludes EPC A, B & C (assumes no need to upgrade)
Adding these back in would increase the cost… obvs
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 129.0 312.0 354.5 470.5 448.8 6350.0
## N elec meters
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 422.0 533.0 619.5 716.8 717.0 6351.0
Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table 4.13 reports total retofit costs.
## To retrofit D-E (£m)
## [1] 818.9499
## Number of dwellings: 61575
## To retrofit F-G (£m)
## [1] 60.21068
## Number of dwellings: 2247
## To retrofit D-G (£m)
## [1] 879.1606
## To retrofit D-G (mean per dwelling)
## [1] 13749.17
meanPerLSOA_GBPm | total_GBPm |
5.4 | 879.2 |
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.19 shows the LSOA level retofit costs per dwelling by IMD decile.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.19: LSOA level retofit costs per dwelling by IMD score
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Totals
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Repeat per dwelling
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.20 shows years to pay under Scenario 1 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.689 2.297 2.690 3.074 3.312 15.648
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.20: Years to pay under Scenario 1 (all em issions)
## Median years: 2.69
Figure 4.21 shows years to pay under Scenario 1 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.59 15.09 17.62 19.37 20.44 65.92
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.21: Years to pay under Scenario 1 (energy emissions)
## Median years: 17.62
Figure 4.22 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 4.22: Year 1 outcome if levy is shared equally (all emissions levy)
LSOA11CD | LSOA11NM | WD18NM | retrofitSum | yearsToPay | epc_D_pc | epc_E_pc | epc_F_pc | epc_G_pc |
E01003618 | Newham 012C | Stratford and New Town | 9,349,511.9 | 19.6 | 0.6 | 0.1 | 0.0 | 0.0 |
E01003495 | Newham 025D | Boleyn | 8,990,770.7 | 15.2 | 0.6 | 0.3 | 0.0 | 0.0 |
E01003539 | Newham 029B | East Ham South | 8,598,156.5 | 16.0 | 0.6 | 0.2 | 0.0 | 0.0 |
E01003537 | Newham 024C | East Ham South | 8,479,390.7 | 16.6 | 0.6 | 0.3 | 0.1 | 0.0 |
E01003608 | Newham 028D | Plaistow South | 8,452,678.2 | 18.5 | 0.6 | 0.2 | 0.0 | 0.0 |
E01003547 | Newham 007A | Forest Gate North | 8,380,421.3 | 14.3 | 0.6 | 0.3 | 0.0 | 0.0 |
E01003574 | Newham 017D | Green Street West | 8,283,186.3 | 15.6 | 0.5 | 0.2 | 0.0 | 0.0 |
E01003548 | Newham 006B | Forest Gate North | 8,233,317.9 | 17.3 | 0.5 | 0.1 | 0.0 | 0.0 |
E01003604 | Newham 031D | Plaistow South | 8,092,600.4 | 18.0 | 0.6 | 0.1 | 0.0 | 0.0 |
E01003543 | Newham 001A | Forest Gate North | 8,084,060.0 | 17.0 | 0.5 | 0.2 | 0.0 | 0.0 |
Figure 4.23 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 4.23: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…
Figure 4.24 shows years to pay under Scenario 2 (all emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.828 3.194 4.486 5.362 6.620 31.424
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.24: Years to pay under Scenario 2 (all em issions)
## Median years: 4.49
Figure 4.25 shows years to pay under Scenario 2 (energy emissions)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.59 15.09 17.62 19.37 20.44 65.92
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 4.25: Years to pay under Scenario 2 (energy emissions)
Figure 4.26 shows the year 1 outcome if levy is shared equally (all emissions levy).
Figure 4.26: Year 1 outcome if levy is shared equally (all emissions levy)
Figure 4.27 shows the year 1 outcome if levy is shared equally (energy emissions levy).
Figure 4.27: Year 1 outcome if levy is shared equally (energy emissions levy)
What happens in Year 2 totally depends on the rate of upgrades…
Figure 4.28 compares pay-back times for the two scenarios - who does the rising block tariff help?
Figure 4.28: Comparing pay-back times across scenarios
I don’t know if this will work…
## Doesn't